The use of ASAR data for class cover identification from small swatches

In this work we address the problem of land cover classification in advanced synthetic aperture radar (ASAR) images. The derivation and assessment of texture features for ASAR image segmentation is investigated using full multidimensional co-occurrence matrices as features. Expansion of local patches in terms of Walsh functions helps identify the optimal distance for the calculation of the co-occurrence matrices. The defined distance agrees with the one chosen by performing exhaustive tests where many distances were tried and the best was chosen from the training data. The well known chi-square test of statistical significance has been used for classification.